Brazilian Forest Dataset: A new dataset to model local biodiversity

The Intergovernmental Panel on Climate Change and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services have emphasised unequivocal evidences about the impact of human actions on climate and biodiversity at alarming rates. In Brazilian terms, 2019 has been marked by controversial discussions among politicians and environmentalists, leading to misinformation and misinterpretations that clearly motivate the continuous collection and scientific analysis of data to support sustainable solutions. Aiming at dealing with this issue, this manuscript brings two contributions: (i) the creation of the Brazilian Forest Dataset, including Brazilian seed plants, Fraction of Absorbed Photosynthetically Active Radiation, meteorological and geographical data composing 8,482 attributes to model and predict 20 vegetation types; and (ii) the feasibility analysis on modelling this dataset in light of supervised machine learning algorithms, so we devise confident results on the Brazilian biodiversity. Experimental results confirm Random Forest and Support Vector Machines successfully adjust models, enabling researchers to predict the occurrence of specific types of vegetation in different regions of Brazil as well as analyse how the prediction accuracy changes along time after the collection of new data. Our contributions bring important tools to support the study on the evolution of the Brazilian biodiversity.

Automated summary

In 2019, controversial discussions among politicians and environmentalists in Brazil highlighted the importance of continuous data collection and scientific analysis. This paper contributes by creating the Brazilian Forest Dataset and analyzing its feasibility with supervised machine learning algorithms, providing important tools for studying the evolving Brazilian biodiversity.